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The objective of augmented reality (AR) is to add digital content to naturalimages and videos to create an interactive experience between the user and theenvironment. Scene analysis and object recognition play a crucial role in AR,as they must be performed quickly and accurately. In this study, a new approachis proposed that involves using oriented bounding boxes with a detection andrecognition deep network to improve performance and processing time. Theapproach is evaluated using two datasets: a real image dataset (DOTA dataset)commonly used for computer vision tasks, and a synthetic dataset that simulatesdifferent environmental, lighting, and acquisition conditions. The focus of theevaluation is on small objects, which are difficult to detect and recognise.The results indicate that the proposed approach tends to produce better AveragePrecision and greater accuracy for small objects in most of the testedconditions.

Evaluation of Environmental Conditions on Object Detection using Oriented Bounding Boxes for AR Applications